In common urine sediment analysis, first, a urine sample image is shot using a microscope system. Then, the candidate blocks in the urine sample image are segmented using, for example, an edge detection technology. By removing obvious background blocks from these candidate blocks, blocks to be processed are detected. Next, the blocks to be processed are processed.
Currently, there are mainly two directions of processing the blocks to be processed. The first direction is classification, i.e. directly classifying these blocks to be processed into various visible element (such as a tubular, an epithelium and an erythrocyte) blocks and background blocks that are easy to be confused with visible elements. The other direction is block retrieval, which does not directly classify the blocks to be processed but retrieves blocks similar to the previously stored blocks to be processed in a database. The unique difference with regard to the result of classification lies in that block retrieval may retrieve a plurality of similar blocks to be provided to a user, and thus can provide more information for the user. The user may perform a further selection or judgment in the plurality of similar blocks.
The block retrieval methods proposed in the prior art have, for example, a feature vector minimum distance method. It is assumed that there are n block retrieval features in a block retrieval feature set. With regard to a specific block, n block retrieval features thereof form an n-dimensional block retrieval feature vector. The distance between the n-dimensional block retrieval feature vector of a block to be processed and an n-dimensional block retrieval feature vector of each stored block is calculated, for example, a Euclidean distance. Then, various stored blocks are arranged successively in an ascending order according to the Euclidean distances of the blocks to be processed and are taken as block retrieval results. In this method, since a large number of blocks are stored in a memory (otherwise, there is no sense to retrieve), a large amount of calculation needs to be done so as to calculate the Euclidean distances between the blocks and the blocks to be processed one by one.